vllm/docs/source/contributing/model/multimodal.md

(supports-multimodal)=

Multi-Modal Support

This document walks you through the steps to extend a basic model so that it accepts multi-modal inputs.

1. Update the base vLLM model

It is assumed that you have already implemented the model in vLLM according to these steps. Further update the model as follows:

• Reserve a keyword parameter in {meth}~torch.nn.Module.forward for each input tensor that corresponds to a multi-modal input, as shown in the following example:

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        kv_caches: List[torch.Tensor],
        attn_metadata: AttentionMetadata,
  +     pixel_values: torch.Tensor,
    ) -> SamplerOutput:
  

  More conveniently, you can simply pass **kwargs to the {meth}~torch.nn.Module.forward method and retrieve the keyword parameters for multimodal inputs from it.

• Implement {meth}~vllm.model_executor.models.interfaces.SupportsMultiModal.get_multimodal_embeddings that returns the embeddings from running the multimodal inputs through the multimodal tokenizer of the model. Below we provide a boilerplate of a typical implementation pattern, but feel free to adjust it to your own needs.

  class YourModelForImage2Seq(nn.Module):
      ...
  
      def _process_image_input(self, image_input: YourModelImageInputs) -> torch.Tensor:
  
          assert self.vision_encoder is not None
          image_features = self.vision_encoder(image_input)
          return self.multi_modal_projector(image_features)
  
      def get_multimodal_embeddings(self, **kwargs: object) -> Optional[NestedTensors]:
  
          # Validate the multimodal input keyword arguments
          image_input = self._parse_and_validate_image_input(**kwargs)
          if image_input is None:
              return None
  
          # Run multimodal inputs through encoder and projector
          vision_embeddings = self._process_image_input(image_input)
          return vision_embeddings
  

  :::{important} The returned multimodal_embeddings must be either a 3D {class}torch.Tensor of shape (num_items, feature_size, hidden_size), or a list / tuple of 2D {class}torch.Tensor's of shape (feature_size, hidden_size), so that multimodal_embeddings[i] retrieves the embeddings generated from the i-th multimodal data item (e.g, image) of the request. :::

• Implement {meth}~vllm.model_executor.models.interfaces.SupportsMultiModal.get_input_embeddings to merge multimodal_embeddings with text embeddings from the input_ids. If input processing for the model is implemented correctly (see sections below), then you can leverage the utility function we provide to easily merge the embeddings.

  from .utils import merge_multimodal_embeddings
  
  class YourModelForImage2Seq(nn.Module):
      ...
  
      def get_input_embeddings(
          self,
          input_ids: torch.Tensor,
          multimodal_embeddings: Optional[NestedTensors] = None,
      ) -> torch.Tensor:
  
          # `get_input_embeddings` should already be implemented for the language 
          # model as one of the requirements of basic vLLM model implementation.
          inputs_embeds = self.language_model.get_input_embeddings(input_ids)
  
          if multimodal_embeddings is not None:
              inputs_embeds = merge_multimodal_embeddings(
                  input_ids=input_ids, 
                  inputs_embeds=inputs_embeds, 
                  multimodal_embeddings=multimodal_embeddings,
                  placeholder_token_id=self.config.image_token_index)
  
          return inputs_embeds
  

• Once the above steps are done, update the model class with the {class}~vllm.model_executor.models.interfaces.SupportsMultiModal interface.

  + from vllm.model_executor.models.interfaces import SupportsMultiModal
  
  - class YourModelForImage2Seq(nn.Module):
  + class YourModelForImage2Seq(nn.Module, SupportsMultiModal):
  

  :::{note} The model class does not have to be named {code}*ForCausalLM. Check out the HuggingFace Transformers documentation for some examples. :::

2. Specify processing information

Next, create a subclass of {class}~vllm.multimodal.processing.BaseProcessingInfo to provide basic information related to HF processing.

Maximum number of input items

You need to override the abstract method {meth}~vllm.multimodal.processing.BaseProcessingInfo.get_supported_mm_limits to return the maximum number of input items for each modality supported by the model.

For example, if the model supports any number of images but only one video per prompt:

def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
    return {"image": None, "video": 1}

Maximum number of placeholder feature tokens

Also, override the abstract method {meth}~vllm.multimodal.processing.BaseProcessingInfo.get_mm_max_tokens_per_item to return the maximum number of placeholder feature tokens per input item for each modality.

When calling the model, the output embeddings from the visual encoder are assigned to the input positions containing placeholder feature tokens. Therefore, the number of placeholder feature tokens should be equal to the size of the output embeddings.

:::::{tab-set} ::::{tab-item} Basic example: LLaVA :sync: llava

Looking at the code of HF's LlavaForConditionalGeneration:

# https://github.com/huggingface/transformers/blob/v4.47.1/src/transformers/models/llava/modeling_llava.py#L530-L544
n_image_tokens = (input_ids == self.config.image_token_index).sum().item()
n_image_features = image_features.shape[0] * image_features.shape[1]

if n_image_tokens != n_image_features:
    raise ValueError(
        f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
    )
special_image_mask = (
    (input_ids == self.config.image_token_index)
    .unsqueeze(-1)
    .expand_as(inputs_embeds)
    .to(inputs_embeds.device)
)
image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)

The number of placeholder feature tokens per image is image_features.shape[1]. image_features is calculated inside the get_image_features method:

# https://github.com/huggingface/transformers/blob/v4.47.1/src/transformers/models/llava/modeling_llava.py#L290-L300
image_outputs = self.vision_tower(pixel_values, output_hidden_states=True)

selected_image_feature = image_outputs.hidden_states[vision_feature_layer]
if vision_feature_select_strategy == "default":
    selected_image_feature = selected_image_feature[:, 1:]
elif vision_feature_select_strategy == "full":
    selected_image_feature = selected_image_feature
else:
    raise ValueError(f"Unexpected select feature strategy: {self.config.vision_feature_select_strategy}")
image_features = self.multi_modal_projector(selected_image_feature)
return image_features

We can infer that image_features.shape[1] is based on image_outputs.hidden_states.shape[1] from the vision tower (CLIPVisionModel for the llava-hf/llava-1.5-7b-hf model). Moreover, we only need the sequence length (the second dimension of the tensor) to get image_features.shape[1]. The sequence length is determined by the initial hidden states in CLIPVisionTransformer since the attention mechanism doesn't change the sequence length of the output hidden states.

# https://github.com/huggingface/transformers/blob/v4.47.1/src/transformers/models/clip/modeling_clip.py#L1094-L1102
hidden_states = self.embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
hidden_states = self.pre_layrnorm(hidden_states)

encoder_outputs = self.encoder(
    inputs_embeds=hidden_states,
    output_attentions=output_attentions,
    output_hidden_states=output_hidden_states,
    return_dict=return_dict,
)

To find the sequence length, we turn to the code of CLIPVisionEmbeddings:

# https://github.com/huggingface/transformers/blob/v4.47.1/src/transformers/models/clip/modeling_clip.py#L247-L257
target_dtype = self.patch_embedding.weight.dtype
patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype))  # shape = [*, width, grid, grid]
patch_embeds = patch_embeds.flatten(2).transpose(1, 2)

class_embeds = self.class_embedding.expand(batch_size, 1, -1)
embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
if interpolate_pos_encoding:
    embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
else:
    embeddings = embeddings + self.position_embedding(self.position_ids)
return embeddings

We can infer that embeddings.shape[1] == self.num_positions, where

# https://github.com/huggingface/transformers/blob/v4.47.1/src/transformers/models/clip/modeling_clip.py#L195-L196
self.num_patches = (self.image_size // self.patch_size) ** 2
self.num_positions = self.num_patches + 1

Overall, the number of placeholder feature tokens for an image can be calculated as:

def get_num_image_tokens(
    self,
    *,
    image_width: int,
    image_height: int,
) -> int:
    hf_config = self.get_hf_config()
    hf_processor = self.get_hf_processor()

    image_size = hf_config.vision_config.image_size
    patch_size = hf_config.vision_config.patch_size

    num_image_tokens = (image_size // patch_size) ** 2 + 1
    if hf_processor.vision_feature_select_strategy == "default":
        num_image_tokens -= 1

    return num_image_tokens

Notice that the number of image tokens doesn't depend on the image width and height. So, we can calculate the maximum number of image tokens using any image size:

def get_image_size_with_most_features(self) -> ImageSize:
    hf_config = self.get_hf_config()
    width = height = hf_config.image_size
    return ImageSize(width=width, height=height)

def get_max_image_tokens(self) -> int:
    target_width, target_height = self.get_image_size_with_most_features()

    return self.get_num_image_tokens(
        image_width=target_width,
        image_height=target_height,
    )

And thus, we can override the method as:

def get_mm_max_tokens_per_item(self, seq_len: int) -> Mapping[str, int]:
    return {"image": self.get_max_image_tokens()}

:::{note} Our actual code is more abstracted to support vision encoders other than CLIP. :::

:::: :::::

3. Specify dummy inputs

Then, inherit {class}~vllm.multimodal.profiling.BaseDummyInputsBuilder to construct dummy inputs for HF processing as well as memory profiling.

For memory profiling

Override the abstract method {meth}~vllm.multimodal.profiling.BaseDummyInputsBuilder.get_dummy_processor_inputs to construct dummy inputs for memory profiling. This dummy input should result in the worst-case memory usage of the model so that vLLM can reserve the correct amount of memory for it.

Assuming that the memory usage increases with the number of tokens, the dummy input can be constructed based on the code for {meth}~vllm.multimodal.processing.BaseProcessingInfo.get_mm_max_tokens_per_item.

::::{tab-set} :::{tab-item} Basic example: LLaVA :sync: llava Making use of the get_image_size_with_most_features method implemented in the previous section:

def get_dummy_processor_inputs(
    self,
    seq_len: int,
    mm_counts: Mapping[str, int],
) -> ProcessorInputs:
    num_images = mm_counts.get("image", 0)

    processor = self.info.get_hf_processor()
    image_token = processor.image_token
  
    hf_config = self.get_hf_config()
    target_width, target_height = self.info.get_image_size_with_most_features()

    mm_data = {
        "image":
        self._get_dummy_images(width=target_width,
                               height=target_height,
                               num_images=num_images)
    }

    return ProcessorInputs(
        prompt_text=image_token * num_images,
        mm_data=mm_data,
    )

::: ::::

4. Specify processing details

Afterwards, create a subclass of {class}~vllm.multimodal.processing.BaseMultiModalProcessor to fill in the missing details about HF processing.

:::{seealso} Multi-Modal Data Processing :::

Multi-modal fields

Override {class}~vllm.multimodal.processing.BaseMultiModalProcessor._get_mm_fields_config to return a schema of the tensors outputted by the HF processor that are related to the input multi-modal items.

:::::{tab-set} ::::{tab-item} Basic example: LLaVA :sync: llava

Looking at the model's forward method:

# https://github.com/huggingface/transformers/blob/v4.47.1/src/transformers/models/llava/modeling_llava.py#L387-L404
def forward(
    self,
    input_ids: torch.LongTensor = None,
    pixel_values: torch.FloatTensor = None,
    attention_mask: Optional[torch.Tensor] = None,
    position_ids: Optional[torch.LongTensor] = None,
    past_key_values: Optional[List[torch.FloatTensor]] = None,
    inputs_embeds: Optional[torch.FloatTensor] = None,
    vision_feature_layer: Optional[int] = None,
    vision_feature_select_strategy: Optional[str] = None,
    labels: Optional[torch.LongTensor] = None,
    use_cache: Optional[bool] = None,
    output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None,
    cache_position: Optional[torch.LongTensor] = None,
    num_logits_to_keep: int = 0,
) -> Union[Tuple, LlavaCausalLMOutputWithPast]:

The only related keyword argument is pixel_values which directly corresponds to input images. The shape of pixel_values is (N, C, H, W) where N is the number of images. So, we override the method as follows:

def _get_mm_fields_config(
    self,
    hf_inputs: BatchFeature,
    hf_processor_mm_kwargs: Mapping[str, object],
) -> Mapping[str, MultiModalFieldConfig]:
    return dict(
        pixel_values=MultiModalFieldConfig.batched("image"),
    )

:::{note} Our actual code additionally supports pre-computed image embeddings, which can be passed to be model via the image_embeds argument. :::

:::: :::::

Prompt replacements

Override {class}~vllm.multimodal.processing.BaseMultiModalProcessor._get_prompt_replacements to return a list of {class}~vllm.multimodal.processing.PromptReplacement instances.

Each {class}~vllm.multimodal.processing.PromptReplacement instance specifies a find-and-replace operation performed by the HF processor.

::::{tab-set} :::{tab-item} Basic example: LLaVA :sync: llava

Looking at HF's LlavaProcessor:

# https://github.com/huggingface/transformers/blob/v4.47.1/src/transformers/models/llava/processing_llava.py#L167-L170
prompt_strings = []
for sample in text:
    sample = sample.replace(self.image_token, self.image_token * num_image_tokens)
    prompt_strings.append(sample)

It simply repeats each input image_token a number of times equal to the number of placeholder feature tokens (num_image_tokens). Based on this, we override the method as follows:

def _get_prompt_replacements(
    self,
    mm_items: MultiModalDataItems,
    hf_processor_mm_kwargs: Mapping[str, object],
    out_mm_kwargs: MultiModalKwargs,
) -> list[PromptReplacement]:
    hf_config = self.info.get_hf_config()
    image_token_id = hf_config.image_token_index

    def get_replacement(item_idx: int):
        images = mm_items.get_items("image", ImageProcessorItems)

        image_size = images.get_image_size(item_idx)
        num_image_tokens = self.info.get_num_image_tokens(
            image_width=image_size.width,
            image_height=image_size.height,
        )

        return [image_token_id] * num_image_tokens

    return [
        PromptReplacement(
            modality="image",
            target=[image_token_id],
            replacement=get_replacement,
        ),
    ]

::: ::::

5. Register processor-related classes

After you have defined {class}~vllm.multimodal.processing.BaseProcessingInfo (Step 2), {class}~vllm.multimodal.profiling.BaseDummyInputsBuilder (Step 3), and {class}~vllm.multimodal.processing.BaseMultiModalProcessor (Step 4), decorate the model class with {meth}MULTIMODAL_REGISTRY.register_processor <vllm.multimodal.registry.MultiModalRegistry.register_processor> to register them to the multi-modal registry:

  from vllm.model_executor.models.interfaces import SupportsMultiModal
+ from vllm.multimodal import MULTIMODAL_REGISTRY

+ @MULTIMODAL_REGISTRY.register_processor(YourMultiModalProcessor,
+                                         info=YourProcessingInfo,
+                                         dummy_inputs=YourDummyInputsBuilder)
  class YourModelForImage2Seq(nn.Module, SupportsMultiModal):